Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer
Background: Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated wi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Translational Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523323002413 |
| _version_ | 1797355317353250816 |
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| author | Qiuli Zhu Hua Dai Feng Qiu Weiming Lou Xin Wang Libin Deng Chao Shi |
| author_facet | Qiuli Zhu Hua Dai Feng Qiu Weiming Lou Xin Wang Libin Deng Chao Shi |
| author_sort | Qiuli Zhu |
| collection | DOAJ |
| description | Background: Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence. Methods: We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance. Results: Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients. Conclusion: Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer. |
| first_indexed | 2024-03-08T14:09:24Z |
| format | Article |
| id | doaj.art-cf7d6e2f82a6482da3173b8e14bb470f |
| institution | Directory Open Access Journal |
| issn | 1936-5233 |
| language | English |
| last_indexed | 2024-03-08T14:09:24Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj.art-cf7d6e2f82a6482da3173b8e14bb470f2024-01-15T04:22:15ZengElsevierTranslational Oncology1936-52332024-02-0140101855Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancerQiuli Zhu0Hua Dai1Feng Qiu2Weiming Lou3Xin Wang4Libin Deng5Chao Shi6Department of Genetics, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Oncology, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, No.7889 of Changdong avenue, Gaoxin District, Nanchang, Jiangxi, ChinaThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, ChinaQueen Mary School of Nanchang University, Nanchang University, Nanchang, ChinaJiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, ChinaDepartment of Oncology, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, No.7889 of Changdong avenue, Gaoxin District, Nanchang, Jiangxi, China; Corresponding author.Background: Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence. Methods: We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance. Results: Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients. Conclusion: Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.http://www.sciencedirect.com/science/article/pii/S1936523323002413Ovarian cancerArtificial intelligence technologyWhole-slide images featuresIntra-tumor heterogeneityDrug resistance |
| spellingShingle | Qiuli Zhu Hua Dai Feng Qiu Weiming Lou Xin Wang Libin Deng Chao Shi Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer Translational Oncology Ovarian cancer Artificial intelligence technology Whole-slide images features Intra-tumor heterogeneity Drug resistance |
| title | Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer |
| title_full | Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer |
| title_fullStr | Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer |
| title_full_unstemmed | Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer |
| title_short | Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer |
| title_sort | heterogeneity of computational pathomic signature predicts drug resistance and intra tumor heterogeneity of ovarian cancer |
| topic | Ovarian cancer Artificial intelligence technology Whole-slide images features Intra-tumor heterogeneity Drug resistance |
| url | http://www.sciencedirect.com/science/article/pii/S1936523323002413 |
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